The digital camera, cheap digital storage, and extensive network connectivity through the Internet have brought about a large growth in the number, size, and access of distributed photo collections. However as the availability of digital photos and digital photo collections grows, searching for particular photos or photos containing particular characteristics becomes increasingly cumbersome.
Individuals may organize their digital photo collections in folders by album name or by date. However, one may often want to access photos across these folders. For example, one may want to find photos of a particular individual in a collection. To facilitate such search activity based on content of photographs, a number of techniques may be used. The tagging of each photograph with one or more keywords is one such technique.
Generally, the tagging of each photograph is done by the user, who manually provides the tags or tag words. In addition, such tags may be derived from information related to each photograph, such as, date, album or folder information. However, these approaches require significant user input, and generally do not scale well to large digital photo collections. The automatic tagging of photos using automatic face recognition techniques is a promising approach to achieving comprehensive tagging of large photo collections.
Automatic face recognition, in general, functions in two stages: a face detection stage, and a face recognition stage. The former can be accomplished by automatically picking out faces in a photograph based on general facial characteristics. The latter may include the comparison of the detected faces against one or more of facial images that have been previously recognized. The accuracy of the second stage increases when there are multiple identified and confirmed images of a particular face, against which a newly detected face can be compared.
The effectiveness of automatic face recognition in large digital image collections can be limited due to not having a sufficient number and variety of facial images of each person being identified and confirmed by a user, and due to erroneous identification. The presently available interfaces that allow a user to identify and confirm faces found in digital photo collections attempt to make it easier for the user to identify many facial images at a time. For example, one interface may present the user with facial images potentially belonging to a particular identified person, arranged in one or more rows, and ask the user to either confirm or reject that system-generated classification. Another interface may present the user with one or more naming options for a particular face newly detected in a photo. In the first case, for example, the user is tasked with deleting each individual face that does not belong to the listed collection. In the second case, the user is tasked with tagging each individual image with a name. In both of the above cases, it is still cumbersome to weed out images and name individual images. More user-friendly and efficient methods are necessary to make the task of identifying multiple images at a time convenient and efficient, such that automatic face recognition may be exercised across the entire digital photo collection.
Users need a flexible capability that would automate much of the process. Automatic face recognition, leveraging a larger variety of images of a particular person, would be more capable of categorizing and arranging detected facial images for user confirmation, such that the confirmation process would be made easier to the user, and thereby facilitating the tagging of larger numbers of images.